{"as_of":"2026-08-08T20:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b371396d89450295ee8c5ce464c1649df1bb4e5e9dc0cd1110667abeb3f92a2d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:50:29.364466Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T13:29:51.141032Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2209.15106","last_updated":"2022-09-29T21:24:26Z","snapshot_observed_at":"2026-07-06T13:57:58.305779Z","submitted_at":"2022-09-29T21:24:26Z","title":"Restricted Strong Convexity of Deep Learning Models with Smooth Activations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15106","snapshot_observed_at":"2026-08-07T04:50:29.364466Z","title":"Restricted strong convexity of deep learning models with smooth activations, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.09679","last_updated":"2026-06-16T10:53:25Z","snapshot_observed_at":"2026-08-07T04:40:22.501218Z","submitted_at":"2025-06-11T12:53:09Z","title":"Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T04:50:29.364466Z"},"links":{"cited_paper":"/paper/2209.15106","citing_paper":"/paper/2506.09679"},"observation_digest":"sha256:78e5417e6f8456afe43fdb7d369e989d3d0a8614e5059aec4c8ea1935a531b40","observation_id":"7c486b88-4c1f-44f1-a10b-85c01ed3e80d","resolution":{"observed_at":"2026-08-07T04:50:29.364466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15106","last_updated":"2022-09-29T21:24:26Z","snapshot_observed_at":"2026-07-06T13:57:58.305779Z","submitted_at":"2022-09-29T21:24:26Z","title":"Restricted Strong Convexity of Deep Learning Models with Smooth Activations","version":1},"cited_work":{"arxiv_id":"2209.15106","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15106","snapshot_observed_at":"2026-07-04T13:29:51.141032Z","title":"arXiv preprint arXiv:2209.15106 , year=","venue":null,"work_id":"68039838-11c0-43ae-b712-09622ab13abb","year":null},"citing_paper":{"arxiv_id":"2606.26749","last_updated":"2026-06-25T08:33:34Z","snapshot_observed_at":"2026-08-01T16:00:20.311664Z","submitted_at":"2026-06-25T08:33:34Z","title":"Structure Before Collapse: Transient semantic geometry in next-token prediction","version":1},"reference_index":203,"source":"arxiv_source","source_observed_at":"2026-06-26T05:14:07.208255Z"},"links":{"cited_paper":"/paper/2209.15106","citing_paper":"/paper/2606.26749"},"observation_digest":"sha256:50357f7500b58de16384f8e04c775088a73bfea59d7649f6f352769d8288f8e5","observation_id":"bf70c4f3-ce04-4ce1-8cc9-5491804e4b3a","resolution":{"observed_at":"2026-07-04T13:29:51.142534Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2209.15106/citation-record","integrity":"/paper/2209.15106/integrity","json":"/paper/2209.15106/citation-record.json","paper":"/paper/2209.15106"},"outbound":[],"paper":{"arxiv_id":"2209.15106","last_updated":"2022-09-29T21:24:26Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T13:57:58.305779Z","submitted_at":"2022-09-29T21:24:26Z","title":"Restricted Strong Convexity of Deep Learning Models with Smooth Activations"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.15106."}